Download Seascape genetics along a steep cline: using genetic patterns to test

Molecular Ecology (2010) 19, 3692–3707
doi: 10.1111/j.1365-294X.2010.04694.x
Seascape genetics along a steep cline: using genetic
patterns to test predictions of marine larval dispersal
HEATHER M. GALINDO,*1 ANNA S. PFEIFFER-HERBERT,† MARGARET A. MCMANUS,‡
Y I C H A O , § F E I C H A I – and S T E P H E N R . P A L U M B I *
*Department of Biology, Stanford University, Hopkins Marine Station, 120 Ocean View Blvd., Pacific Grove, CA 93950, USA,
†Graduate School of Oceanography, University of Rhode Island, South Ferry Road, Narragansett, RI 02882, USA, ‡Department
of Oceanography, University of Hawaii at Manoa, Marine Sciences Building, 1000 Pope Road, Honolulu, HI 96822, USA,
§Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA, –School of
Marine Sciences, 453 Aubert Hall, University of Maine, Orono, ME 04469, USA
Abstract
Coupled biological and physical oceanographic models are powerful tools for studying
connectivity among marine populations because they simulate the movement of larvae
based on ocean currents and larval characteristics. However, while the models
themselves have been parameterized and verified with physical empirical data, the
simulated patterns of connectivity have rarely been compared to field observations. We
demonstrate a framework for testing biological-physical oceanographic models by using
them to generate simulated spatial genetic patterns through a simple population genetic
model, and then testing these predictions with empirical genetic data. Both agreement
and mismatches between predicted and observed genetic patterns can provide insights
into mechanisms influencing larval connectivity in the coastal ocean. We use a highresolution ROMS-CoSINE biological-physical model for Monterey Bay, California
specifically modified to simulate dispersal of the acorn barnacle, Balanus glandula.
Predicted spatial genetic patterns generated from both seasonal and annual connectivity
matrices did not match an observed genetic cline in this species at either a mitochondrial
or nuclear gene. However, information from this mismatch generated hypotheses testable
with our modelling framework that including natural selection, larval input from a
southern direction and ⁄ or increased nearshore larval retention might provide a better fit
between predicted and observed patterns. Indeed, moderate selection and a range of
combined larval retention and southern input values dramatically improve the fit
between simulated and observed spatial genetic patterns. Our results suggest that
integrating population genetic models with coupled biological-physical oceanographic
models can provide new insights and a new means of verifying model predictions.
Keywords: genetics, marine connectivity, numerical modelling, oceanography
Received 19 November 2009; revision received 2 March 2010; accepted 15 March 2010
Introduction
Landscape genetics takes into account the impact of
geographic and population features that affect the way
organisms and their genes move among localities
Correspondence: Stephen R. Palumbi, Fax: +1 831 655 6215;
E-mail: [email protected]
1
Present address: School of Aquatic and Fishery Sciences,
University of Washington, Box 355020, Seattle, WA 98195-5020,
USA.
(Manel et al. 2003). Dispersal barriers are not as immediately obvious in the marine environment (Galindo
et al. 2006), and research interest has focused on the
way ocean currents provide dispersal conveyer belts
from place to place. Larval connections among spatially separate marine populations can have significant
impacts on the local demography and genetic diversity
of these populations, thereby affecting their evolutionary adaptability to environmental change (Kinlan
& Gaines 2003; Gaines et al. 2007). Historically, the
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S E A S C A P E G E N E T I C S A N D C L I N E S 3693
probability that populations were connected by larval
dispersal was estimated based on knowledge of average seasonal circulation patterns and the amount of
time it took larvae to physiologically develop in the
lab (reviewed in Shanks et al. 2003). More recently,
physical oceanographic circulation models verified by
empirical data allow for more precise simulation of
larval movement among locations (Cowen et al. 2006;
Pfeiffer-Herbert et al. 2007; Werner et al. 2007; Treml
et al. 2008). These models often take into account larval biology in the form of pelagic larval duration,
behaviour (usually changes in vertical depth over
time), growth rates, mortality, and habitat preferences.
Data on larval behaviour and growth are often derived
from laboratory studies (see Pfeiffer-Herbert et al. 2007
for an example). The result is a series of larval movement predictions over short spatial and temporal
scales that can be combined across many simulations
to produce an estimate of the probability that a larva
produced at one place disperses and settles at another.
Other methods such as graph theory (Treml et al.
2008) produce similar results: a connectivity matrix
that estimates the relative directional dispersal among
locations within a complex population.
These connectivity matrices can be compared to
empirical data in multiple ways: (i) The connectivity
matrix generated from a coupled bio-physical oceanographic model may be compared to a connectivity
matrix developed from direct observations of dispersal;
or (ii) the connectivity matrix generated from a coupled
bio-physical oceanographic model may be used to generate predictions of empirical patterns that can then be
compared to field-based observational data. The first
approach is more appropriate for studies where actual
counts of the relative number of individuals moving
among locations are known. This is the case when
larger individuals are physically tagged with marker,
archival, or satellite tags (Schaefer et al. 2007; Weng
et al. 2007). The use of otolith or statolith microchemistry and genetic assignment tests to identify larval origins also falls into this category (Thorrold et al. 2001,
2007; Rooker et al. 2008). However, the most abundant
sources of readily available empirical connectivity data
are population genetic studies. Because genetic data
integrate the signal of realized connectivity over time,
and because gene flow can occur across generations via
a stepping stone pattern of movement, it is not straightforward to use genetic data to create a connectivity
matrix. Instead, it is more feasible to use a connectivity
matrix to predict long-term average genetic patterns
that develop over many generations (Galindo et al.
2006). This comparison of physical predictions of migration to empirical measures based on gene frequency differences has defined the field of landscape genetics, and
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its marine counterpart, seascape genetics (Galindo et al.
2006; Selkoe et al. 2008).
The prediction of spatial genetic patterns has often
been based on simple models of population structure
such as the Island model (Wright 1931) or Isolationby-Distance model (Rousset 1997). Though Island
model analyses have been extremely useful in population genetics, few natural populations completely meet
the assumptions of the model, and as a result, the
calculations derived from this model have limited
practical uses (Whitlock & McCauley 1999). Likewise,
Isolation-by-Distance models have provided important
alternatives to a strictly Island model (Rousset 1997),
and have allowed new insights into the ecology and
conservation of natural populations (Palumbi 2003).
However, interpretations depend heavily on ecological
and reproductive data, such as local effective densities,
that are rarely available (Pinsky et al. 2010). One solution, the recently developed Isolation-by-Resistance
models, allows the prediction of spatial and temporal genetic patterns in a heterogeneous landscape,
although these models rely on spatially explicit habitat
maps that are difficult to generate for the temporally
unstable marine environment (McRae 2006). In parallel,
the development of coupled spatially explicit dispersal
and population genetic models have a more extensive
history in terrestrial systems (as reviewed by Epperson
et al. 2010). Instead, in marine systems, we can use the
connectivity matrices produced by empirically-based
biological-physical oceanographic models to make
genetic predictions for dispersal patterns in a complex
seascape.
For example, a previous study of broadcast spawning
corals in the Caribbean Sea used this approach and
found correspondence between simulated and observed
genetic patterns at a regional scale (Galindo et al. 2006).
This was the first example of using connectivity matrices produced by a coupled biological-physical oceanographic model to directly simulate genetic patterns that
are then compared to empirical data, but the recent
release of a new genetic model also applied to the
Caribbean suggests this approach is on the rise (Kool
et al. 2009). Baums et al. (2006) found that simulated
coral larvae did not cross an ocean passage between the
eastern and western Caribbean, a dispersal break that
matched a significant genetic difference between populations east and west of this divide. Other studies have
compared spatial genetic patterns to observed oceanographic features. Selkoe et al. (2006) were able to correlate the genetic signature of recruiting cohorts of kelp
bass (Paralabrax clathratus) to changes in current flows
in the Santa Barbara Channel that were likely coming
from different larval sources. Veliz et al. (2006) used a
genetic model balancing fitness values and dispersal
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rates of two allozyme loci under selection in the acorn
barnacle Semibalanus balanoides to estimate asymmetrical
larval exchange in the Gulf of St Lawrence. The resulting predictions were consistent with previous data on
larval dispersal patterns and oceanographic circulation
in the region. Gilg & Hilbish (2003) were able to find
similar patterns of connectivity for blue mussels
(Mytilus edulis and M. galloprovincialis) in southwest
England using both a circulation model and empirical
genetic data collect from recently settled recruits. In this
Special Issue, Selkoe et al. (2010) use correlations among
genetic patterns in three marine species in the Southern
California Bight and environmental data to show that
kelp cover and ocean circulation patterns affect the spatial genetic structuring of these populations.
Successful implementation of this seascape genetics
approach requires a physical oceanographic model driven by empirical data, the ability to simulate biologically-relevant larval characteristics, and a spatially
explicit empirical genetic dataset to which the predicted
patterns can be compared. Although all of these elements are not likely to be available for all species at all
locations, we can use existing opportunities to investigate the identity and magnitude of factors likely to
affect population connectivity for species having a similar spatial distribution and life history. To that end,
initial applications of this modelling framework should
focus on exploring the sensitivity of predicted and
observed data matches to changing a variety of parameters. Further, where there is a mismatch, the nature of
the mismatch should be used to inform the choice
of how and which factors of the model should be
modified.
In order to demonstrate this approach, we present a
comparison between simulated connectivity patterns for
the intertidal barnacle Balanus glandula in Central California with empirical genetic data from the same species. Biologically relevant models including growth
rates based on food availability and ontogenetic vertical
migration have previously been developed for this species (Pfeiffer-Hoyt & McManus 2005; Pfeiffer-Herbert
et al. 2007). These models were then integrated into a
nested-grid Regional Ocean Modelling System (ROMSCoSINE) framework for a region centered on Monterey
Bay, CA. The B. glandula populations in this region have
also been previously shown to exhibit a clinal pattern
in allele frequencies at two loci, the mitochondrial gene
cytochrome oxidase I and the nuclear gene elongation
factor 1 alpha (Sotka et al. 2004). Together, these studies
lay the foundation to test the ability of the coupled biological-physical oceanographic model to predict the
observed clinal pattern. Our goal will be to use the
modelling framework to investigate the factors influencing dispersal in an intertidal marine species living in an
eastern boundary current system with seasonal upwelling.
Materials and methods
Genetic data
Approximately 50 adult individuals were collected at
each of the 13 sampling locations: Pillar Point (PP)*, Pescadero State Beach (PSB), Waddell Creek (WC), Sand Hill
Bluff (SHB), Natural Bridges ⁄ Terrace Point (TP), Capitola
Beach (CB), Moss Landing Jetty (MLJ), Pacific Grove
(PG)*, Asilomar ⁄ Point Piños (APP), Stillwater Cove (SC),
Carmel Point (CP), Sobranes Point (SP), and Andrew
Molera Beach (AM) (details in Table 1 and Fig. 1; * A
subset of these samples were included in Sotka et al.
2004). These samples are concentrated in the center of the
steep genetic cline extending between Cape Mendocino
and Point Conception in B. glandula populations (Sotka
et al. 2004). For each individual, a small amount of cirral
tissue was dissected and placed in 100 lL 10% Chelex100 resin (Bio-Rad Laboratories) solution for DNA extraction (Sotka et al. 2004). A mitochondrial locus (cytochrome oxidase I, COI) and a nuclear locus (elongation
factor 1 alpha, EF1a) were sequenced in forward and in
both directions, respectively, in all individuals. Individual haplotypes for COI were assigned to haplotype
clades (i.e. A, B, or C) based on 386 basepairs of DNA
sequence data according to the criteria in Sotka et al.
(2004). Haplotype clade frequences for EF1a were determined by DNA sequence at basepairs 105–108 relative to
the 156bp fragment in Sotka et al. (2004). (Clade
A=TCCA, Clade B=TCAT, Clade C=CCCA.)
Larval transport model
To simulate pathways of larval transport among the 13
populations from which genetic data were collected, we
used a series of numerical models. An ocean circulation
model (ROMS) coupled with an ecosystem model
(CoSINE) produced fields of horizontal current velocities, temperature and chlorophyll. A larval development
model predicted larval growth rates from temperature
and chlorophyll levels. Larvae also undergo ontogenetic
vertical migration. Finally, a particle-tracking model
used current velocities to advance the position of larvae
over time.
Circulation model
We used the Regional Ocean Modelling System (ROMS;
Shchepetkin & McWilliams 2005) to generate predictions of ocean currents along the central California
coastline. For the Monterey Bay region, ROMS
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Table 1 Frequency of COI-C haplotypes and EF1a-C haplotypes in populations of Balanus glandula sampled along the coast of central California. Clade designations are as in Sotka et al. (2004). Sample sizes (N) are listed separately for each locus. (* Simulated larvae were released 2 km offshore from these locations. †Our collecting permits for barnacles forbid collection of animals within the
borders of PG, California except for at the Hopkins Marine Station (PG). For the Asilomar ⁄ Point Pinos oceanographic station, we
interpolated gene frequencies based on Hopkins (PG) and SC data, from approximately 4 and 15 km away, respectively)
Locality
Latitude, Longitude*
Year
COI (C)
EF1 (C)
COI (n)
EF1 (n)
PP
Pescadero (PSB)
WC
SHB
Natural Bridges ⁄ TP
CB
MLJ
PG
Asilomar ⁄ Pt. Piños (APP)
SWC
CP
SP
AM
37
37
37
36
36
36
36
36
36
36
36
36
36
2002, 2004
2004
2004
2004
2004
2004
2004
2003
2007
2005
2004
2004
2005
0.61
0.71
0.83
0.80
0.87
0.87
0.86
0.90
0.94
0.98
0.91
0.95
0.98
0.40
0.40
0.53
0.71
0.72
0.65
0.63
0.68
0.67
0.66
0.70
0.70
0.78
54
45
41
48
46
39
45
58
–†
43
45
43
44
54
42
48
45.5
37
48
45
58
–†
48
45
44
43
30¢
16¢
05¢
58¢
57¢
58¢
49¢
37¢
38¢
34¢
32¢
27¢
18¢
N,
N,
N,
N,
N,
N,
N,
N,
N,
N,
N,
N,
N,
122
122
122
122
122
121
121
121
121
121
121
121
121
28¢
24¢
16¢
09¢
04¢
57¢
47¢
54¢
56¢
56¢
56¢
56¢
53¢
W
W
W
W
W
W
W
W
W
W
W
W
W
Fig. 1 Map of model domain in central California and location
of larval release sites. Sites with genetic data are PP, PSB, WC,
SHB, TP, CB, MLJ, PG, APP, SWC, CP, SP and AM Beach.
Additional larvae were released along the southern boundary
(SBY) of the model. Inset map shows location of model domain
along the US west coast.
configurations consist of three nested domains: the
entire US West coastal ocean at 15 km resolution, the
central California coastal ocean at 5 km resolution, and
the Monterey Bay region (Fig. 1) at 1.5 km resolution
(Chao et al. 2009; Wang et al. 2009). All three ROMS
configurations have 20 vertical layers and a minimum
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depth of 50 m. In the Monterey Bay region, the shoreward model boundary is on average 2 km offshore of
the actual shoreline due to imprecision in the process of
generating the model grid. The 15, 5 and 1.5 km regional ROMS are nested on-line as a single system and run
simultaneously, exchanging boundary conditions at
every time step of the parent grid. See Chao et al.
(2009) and Wang et al. (2009) for more details about the
ocean model.
The ROMS configuration described above has been
integrated for many seasonal cycles using empirically
measured climatological conditions and forced by
known climatological air-sea fluxes. This manuscript
describes simulations that used data from August 2003
(1 month) and from the period between August 2006
and July 2007 (1 year). During the August 2003 integration, the air-sea fluxes were derived from the Coupled Ocean-Atmosphere Mesoscale Prediction System
(Doyle et al. 2009) at 3 km resolution. Ocean temperature and salinity measurements collected from both
remote sensing (e.g. satellites and aircraft) and in situ
platforms (e.g. ships, gliders, Autonomous Underwater
Vehicles) during the Autonomous Ocean Sampling Network II project of August 2003 were assimilated into
ROMS using a three dimensional variational method (Li
et al. 2008, 2009). During the August 2006–July 2007
integration, we assimilated both the in situ and remotely sensed observations every 6 h (Chao et al. 2008).
The in situ data are derived from gliders, AUVs, moorings, and ship measurements, while the remotely
sensed data are derived from satellite sea surface temperature measurements and high frequency (HF) radar
surface current measurements. Chao et al. (2009)
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compared ROMS model output to salinity data collected
by gliders over depths of 10–50 m at five locations
within the model domain. Correlations between the
model and data were greater than 0.75 and RMS differences were less than 0.1 psu throughout the depth
range (Chao et al. 2009). The velocity fields predicted
by the model also compare well, qualitatively, with
both vertical profiles of velocity and horizontal maps of
surface currents (Chao et al. 2009). The model output
was saved hourly, and the daily snapshots (at 1500
GMT) are used here. The result is a set of predicted
ocean current patterns on grid points spaced 1.5 km
apart throughout the Monterey Bay region.
Ecosystem model
An ecosystem model (Carbon, Silicate, and Nitrogen Ecosystem—CoSINE), developed by Chai et al. (2002), was
embedded into the 3-level nested ROMS. CoSINE consists of 12 compartments: two classes each of phytoplankton (P1, P2) and zooplankton (Z1, Z2), dissolved
inorganic nitrogen in the form of nitrate (NO3) and
ammonium (NH4), detritus nitrogen (DN), detritus silicate (DSi), and total CO2. Nitrate and ammonium are
treated as separate nutrients, thus dividing primary production into new production and regenerated production. Below the euphotic zone, sinking particulate organic
matter is converted to inorganic nutrients by a regeneration process similar to the one used by Chai et al. (1996),
in which organic matter decays to ammonium and then is
nitrified to NO3. The modeled chlorophyll concentration
is derived from the phytoplankton biomass (mmol
N m)3), converted to mg m)3 using a nominal gram chlorophyll-to-molar nitrogen ratio of 1.64, which corresponding to a chlorophyll-to-carbon mass ratio of 1:50
and a carbon-to-nitrogen molar ratio of 7.3.
The CoSINE model has been coupled with the ROMS
for the Pacific basin and evaluated against available
observations for different regions in the Pacific Ocean
(Chai et al. 2009). The modeled nutrients and phytoplankton dynamics and carbon fluxes have been analysed in response to seasonal and interannual climate
variation (Bidigare et al. 2009; Fujii et al. 2009; Liu &
Chai 2009). For the Monterey Bay ROMS-CoSINE model
simulations, all the biogeochemical fields (e.g. nutrients,
planktons, and TCO2) were initialized from the Pacific
ROMS-CoSINE model simulation. Boundary conditions
for the biogeochemical components were also used from
the Pacific ROMS-CoSINE outputs.
The patterns of surface chlorophyll predicted by
CoSINE reproduce patterns observed in remotely
sensed chlorophyll data obtained from the MODIS
satellite (http://oceancolor.gsfc.nasa.gov/cgi/l3) reasonably well. Mean chlorophyll levels, however, were
lower in the model than in the satellite data. In situ data
were available from nine vertical profiles collected in
August 2006 between 36.6˚ N and 37˚ N (R. Kudela,
Univ. of California, Santa Cruz, unpublished data).
Comparisons of CoSINE output and the in situ chlorophyll data also revealed an offset in absolute levels of
chlorophyll below the sea surface. To correct for this
under-prediction of chlorophyll concentration in the
model, we augmented the modeled chlorophyll by a
scaling factor of 3.5, which was the median difference
between the in situ data and the model. Although the
duration of the larval period is sensitive to variations in
chlorophyll (Pfeiffer-Hoyt & McManus 2005), the scaling factor did not have a large impact on predicted larval transport patterns. For example, when the CoSINE
output was increased or decreased by the root mean
square difference between modeled and observed chlorophyll, 96% of larvae settled within 1 km of their original settlement location (Pfeiffer-Herbert et al. 2007).
Larval development model
The development rate of B. glandula larvae is calculated
as in Pfeiffer-Hoyt & McManus (2005). B. glandula larvae develop through six feeding naupliar stages that
are not competent to settle, followed by a non-feeding
cyprid stage that is competent to settle (Brown &
Roughgarden 1985). The length of time that barnacle
larvae spend as nauplii varies with temperature and
food concentration (e.g. Anil & Kurian 1996). The
results of laboratory experiments (Brown & Roughgarden 1995; Hentschel & Emlet 2000; R.B. Emlet, Oregon
Institute of Marine Biology, unpublished data) that
reared B. glandula larvae under varying temperature
and food concentration levels and measured the duration of the naupliar stages were compiled into a lookup
table. The duration of each naupliar stage as a fraction
of total naupliar duration was obtained from laboratory
experiments by Brown & Roughgarden (1985). The larval development subroutine uses temperature from
ROMS and chlorophyll (as a proxy for food concentration) from CoSINE to determine the fraction of naupliar
development that is completed during each model time
step. The larval development subroutine tracks the
naupliar stage achieved by each individual. Temperature and chlorophyll are stored at each 30 min time step
of the particle tracking routine and averaged at the end
of the day. Larval maturation is updated once per day.
Larval depth distribution
Field observations suggest that the depth distributions
of many species of barnacle larvae change through the
naupliar and cyprid stages. In field observations, the
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larvae of many barnacle species, including one in the
Balanus genus, appear to have a similar average depth
range (B. Grantham, personal communication). For
example, the average depth of Balanus improvisus larvae
increases as naupliar stage increases (Bousfield 1955).
Existing observations of B. glandula larvae are not adequate to determine stage-specific depth distributions for
this modelling study, however detailed observations of
larvae of the barnacle Chthamalus spp. were collected in
the Monterey Bay region in 1993–1994 (B. A. Grantham,
unpublished data). Thus, Chthamalus spp. was chosen
as a substitute for B. glandula larval depth distributions
based on our general knowledge of barnacle depth-distributions, and because the recruitment patterns of
Chthamalus spp. and B. glandula are similar in the study
area (Connolly et al. 2001).
The depth distribution of B. glandula larvae was
parameterized after the average depth distribution of
Chthamalus spp., which increases from 0 to 25 m as the
larvae mature (after Pfeiffer-Herbert et al. (2007)). The
cyprid stage returns to the surface layer of the model,
as observed in the field (Grosberg 1982). Larvae are
assumed to be capable of maintaining their preferred
depth, as suggested by comparing averaged swimming
speeds and typical vertical current velocities (Hardy &
Bainbridge 1954; Franks 1992); thus all larvae of a particular larval stage maintain a prescribed depth in the
model. It should be noted that the output of the larval
transport model is not very sensitive to variations in
the stage-specific depths within this depth range (Pfeiffer-Herbert et al. 2007).
Particle-tracking model
In the particle-tracking model, larvae are treated as
particles that are advected by the horizontal current
velocities predicted by ROMS, as described in PfeifferHerbert et al. (2007). Simulated larval trajectories are
calculated by a 4th-order Runge–Kutta integration
scheme with a time step of 30 min. Larvae that cross a
land boundary are returned to their previous water
position. Larvae that cross an open sea boundary are
considered ‘lost’ from the simulation.
Once a larva is competent to settle into its adult habitat, it is tracked until it arrives at a designated habitat
zone, is lost through a sea boundary, or it reaches the
8-week time limit for larval stage duration (Strathmann
et al. 1981). Habitat zones are 10 km2 boxes (5 km in
the along-shore direction and 2 km in the across-shore
direction) placed along the model shoreline boundary.
The 2 km width of habitat zones was selected to
include larval trajectories that approach but never reach
the model shoreline due to boundary effects on the predicted currents. Competent larvae that arrive within a
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habitat zone before the maximum larval period duration are called ‘settlers’.
In order to estimate the amount of time it might take
for a larva to be transported between the shoreline and
the 50 m isobath (or 2 km offshore), we used 7.5-year
time series of current velocity data collected on the 20 m
isobaths in both northern and southern Monterey Bay by
the Partnership for Interdisciplinary Studies of Coastal
Oceans (PISCO) program. Over the whole time series, the
near-surface currents are onshore 46% of the time at both
current measuring stations. Our calculations show that,
on average, it would take 0.6 and 2.8 days for a larva that
hatches into the surface waters to be transported from
the shoreline to the centre of the habitat boxes in northern
and southern Monterey Bay, respectively. This time
frame is similar to the average duration of naupliar stage
I in laboratory studies (Brown & Roughgarden 1985),
which suggests that it is reasonable for the larvae to begin
naupliar stage II at the model boundary. The pre-settlement cyprid larval stage is also found near the surface.
The observed near-surface currents are onshore 37% of
the time series at the northern station and 51% of the
time at the southern station. We estimate transit times of
0.5 and 3 days from the center of the habitat box to the
shoreline during periods of onshore surface flow in
northern and southern Monterey Bay, respectively.
Because these estimated time frames for transit of larvae
through the nearshore zone is short relative to the entire
larval duration, we make the assumption that the larvae
that arrive at a habitat zone are subsequently able to
reach the intertidal zone directly adjacent to the modeled
habitat zone. In the discussion section, we discuss implications of assuming that larval connectivity patterns do
not depend on the nearshore flow patterns that are not
included in the circulation model (please see ‘Local retention’).
Larval transport model simulations
The ‘summer base run’ of the larval transport model
(simulation 1 [L1], Table 2) simulated larval dispersal
between 30 January 2007 and 10 July 2007. A cluster of
450 larvae was released every 7 days, at each of the 13
sites at which we have empirical genetic data (Fig. 1).
In total 157,950 larvae were released. Larvae were
counted as settlers if they matured to the cyprid stage
and if they reached a habitat zone around one of the
larval release sites within 56 days. The ‘annual base
run’ of the larval transport model (simulation 2 [L2],
Table 2) simulated larval dispersal between 15 August
2006 and 10 July 2007. A cluster of 450 larvae was
released every 7 days, at each of the 13 sites at which
we have empirical genetic data (Fig. 1). In total 274,950
larvae were released.
3698 H . M . G A L I N D O E T A L .
Table 2 Larval transport model simulations
Description
Simulation
Dates
Larval release locations
Number of larvae
Summer Base Run
Annual Base Run
Southern sources
Interannual comparison
L1
L2
L3
L4
30 January 2007–10 July 2007
15 August 2006–10 July 2007
15 August 2006–10 July 2007
1 August 2003–31 August 2003
Genetic sampling sites
Genetic sampling sites
Along southern boundary
Genetic sampling sites
157,950
274,950
23,500
23,400
Two additional larval transport model simulations
are reported in this paper. The ‘southern sources run’
of the larval transport model (simulation 3 [L3],
Table 2) was designed to assess the effect of input of
larvae from sources south of the model domain. In this
simulation, larvae were distributed in a cross-shore rectangle beginning 11 km inside the southern boundary of
the model and extending 11 km north (Fig. 1). Larvae
were released weekly and tracked to the 13 habitat
boxes, or to within 2 km of the model shoreline boundary. The ‘interannual variability run’ of the larval transport model (simulation 4 [L4], Table 2), was designed
to assess differences in connectivity patterns for the
same season in two different years. As in the base runs,
but using ROMS-CoSINE output from August 2003, larvae were released weekly from the 13 genetic sampling
sites and tracked for 56 days (Table 2). Because only
1 month of output from ROMS-CoSINE was available
in 2003, the velocity, temperature and chlorophyll fields
were used in a loop (i.e. 31 August was followed by 1
August). Results from the August 2003 simulation are
compared to results from 15 August–15 September 2006
from the yearlong base run (simulation 2 [L2], Table 2).
A
B
Connectivity matrices
At the end of each larval transport simulation, the number of larvae that settled at each of the 13 genetic sampling sites was summed. The contribution of each of
the 13 initial populations to the total settlement at a single population can be determined. Likewise, we can
determine the final arrival place of all larvae released
from a particular population. The simulation results
were placed in a connectivity matrix, a two-dimensional
grid of larval settlement separated by release location
versus settlement location’ (Fig. 2).
Population genetics model
The connectivity matrices based on movement of simulated larvae among the empirical sampling locations
were used to estimate genetic structure. To simulate
mtDNA, we used the empirical frequencies of the
southern haplotype clade C at AM and PP as the input
into the population genetic model. To simulate EF1, we
Fig. 2 Population connectivity matrices for (A) 30 January–10
July 2007 and (B) 15 August 2006–10 July 2007. Columns are
sites of larval release and rows are settlement sites.
used the frequencies of the southern allele C (see Sotka
et al. 2004) as the input into the population genetic
model. Because barnacles are hermaphrodites, all individuals add both mtDNA and nuclear genes to the population. For each generation and each of the 13
sampling locations in Fig. 1, we estimated the gene frequency that results from the combination of local retention, and larval input from elsewhere. The fraction of
settling larvae at j derived from each location i along
2010 Blackwell Publishing Ltd
S E A S C A P E G E N E T I C S A N D C L I N E S 3699
the coast is pij ⁄ pj,total, where pij is the fraction of particles leaving deme i that land at deme j, and pj,total is the
sum of these fractions for deme j. The gene frequency
at location i in generation t (fi(t)) was used to estimate
the new gene frequency at each location j (fj(t + 1)) as
the average of gene frequencies at the different demes
weighted by their fractional contribution to settlement
at deme j
fj ðt þ 1Þ ¼
X
½pij =pj ; total fi ðtÞ:
These deterministic equations do not include changes
due to genetic drift as in previous models (Galindo
et al. 2006) because an initial sensitivity analysis suggested that gene flow in this area was a much more
powerful force than drift. As a result, we are using
these matrices to model the clinal transitions between
northern and southern gene frequencies rather than
using them to model equilibrium levels of genetic differentiation among populations.
In order to model clinal structure, we fixed northern
and southern gene frequencies at both loci according to
the frequencies observed at AM and PP. We added a
small amount of dispersal from these fixed gene pools
into AM and PP. In general, the geographic patterns of
the results we present here had low sensitivity to
changes in the amount of this gene flow into the edges
of the model. We ran the genetic population model for
all sampled populations for 200 generations. Inspection
of the data typically showed that the cline reached a
stable state by 100–150 generations. Here we report
gene frequencies at generation 200, although it should
be noted that in no case do our results change if we
run the model for more generations.
Simulating spatial genetic patterns
The population genetic model was used to predict spatial genetic patterns under six different scenarios
(Table 3). First, we simulated the genetic patterns
resulting from the summer (30 January 2007–2010 July
2007, L1, G1) base run of the coupled biological-physical model (Table 2). Second, we simulated patterns
based on the annual (15 August 2006–10 July 2007, L2,
G2) run. Next, we added three features to the summer
base run connectivity matrix (L1) to test whether the
augmented matrices provided a better match between
the predicted and observed genetic patterns.
The first added feature was additional input of larvae
from the southern boundary of the model based on the
relative amounts of settlement of these larvae predicted
by the annual connectivity matrix generated from the
southern sources simulation (L3, G3) of the biologicalphysical oceanographic model. The distribution of settlement of these southern larvae, S(i), was estimated as
the number larvae settled at location i divided by the
total southern larvae settled. For each location j in
the genetic model, we increased settlement into j from
the south by S(j) · SI · pj,total, where SI is the southern
input as a fraction of the total larval settlement that
occurs during the summer months. When SI = 0.1, for
example, the settlement into the model from southern
sources is equal to 0.10 times the total settlement that
occurs during summer. During our genetic model simulations, we altered southern input (SI) from 0.0 to 1.0 to
investigate the sensitivity of our model to input of larvae from the southern boundary.
The second feature added to the summer connectivity
matrix (L1), which we term a retention multiplier (RM),
was used to represent varying rates of retention of
larvae in their parent population (G4). For all entries in
the diagonal of our connectivity matrix (e.g. the pjj
terms), we multiplied these values by a constant, RM.
We used RM to increase the amount of recruitment
from deme j to j from 1 to 200. Then, we used combinations of both the southern larval input and larval retention features to augment the summer connectivity
matrix (L1, G5).
The third added feature was selection in favor of
individuals carrying alleles characteristic of the southern populations in the summer connectivity matrix (L1,
Table 3 Population genetic model simulations
Description
Spring ⁄ summer connectivity
All year connectivity
Spring ⁄ summer connectivity
Spring ⁄ summer connectivity
Spring ⁄ summer connectivity
15% Southern input + 150x
Spring ⁄ summer connectivity
+ 15% Southern input
+ 150x local retention
+
local retention
+ selection
2010 Blackwell Publishing Ltd
Genetic
simulation
Dates
Larval transport
simulation
G1
G2
G3
G4
G5
30
15
30
30
30
L1
L2
L1 + L3
L1
L1 + L3
G6
30 January 2007–10 July 2007
January 2007–10 July 2007
August 2006–10 July 2007
January 2007–10 July 2007
January 2007–10 July 2007
January 2007–10 July 2007
L1
3700 H . M . G A L I N D O E T A L .
G6). This was done by allowing selection to act on allele
frequencies. We changed the relative frequency of
southern alleles in each population each generation by
p(1 ) p)s ⁄ (1 + ps), where p is the frequency of the southern allele in the model, and s is the selection coefficient
on the southern allele (i.e. relative fitness (W) of the
southern allele is 1 + s (see Hartl and Clark 1997, eq.
6.6)). We make the simple assumption that s is constant
over space within the model, except for the northern
boundary.
Methods for comparing genetic model predictions and
empirical data
We graphed the gene frequencies predicted by the
genetic model for each deme for each generation and
compared them to the observed gene frequencies. To
compare the fit of different hypothetical models to the
data, we used a simple sum of squares, R(fi(t) ) obsi)2,
where obsi is the observed gene frequency at deme i.
Results
Empirical genetic data
Haplotype clade frequencies of COI (NCBI accession
numbers HM028681–HM029141) and EF1a (see
Table S1, Supporting information) shift dramatically
from PP to AM on the Big Sur coast (AM). The sharpest
breaks occur from PSB to WC and Natural Bridges ⁄ TP
in Santa Cruz. Across this distance of approximately
60 km, the C haplotype of COI increases from 71% to
87%, and the C haplotype of elongation factor 1 alpha
increases from 40% to 72% (Table 1). At the southern
end of the sampling range, COI-C is nearly fixed, and
EF1-C occurs at 78% frequency.
Larval transport model
Summer connectivity patterns (simulation L1; Fig. 2A): In
30 January–10 July, the major spawning season for
B. glandula in this region (Gaines et al. 1985; Broitman
et al. 2008), population connectivity was dominated by
southward movement of larvae. In this season, a majority of the larvae from all but two of the populations settled to the south of their release location. Only PG and
APP had more than 50% of their larvae settle to the
north.
Winter connectivity patterns (simulation L2; Fig. S1,
Supporting information): Simulated larval settlement
rates were lower overall in the winter. In contrast to the
summer period, connectivity patterns were more
strongly influenced by northward movement of larvae.
Larvae released from nine of the populations (all but
WC, SHB, CP, and SP) were more likely to settle to the
north of their release location.
Transport from southern sources (simulation L3; Fig. S2,
Supporting information): Most of the larvae released
near the southern boundary were rapidly lost from the
model domain. Few larvae successfully moved as far
north as AM (the most southerly population modelled
in simulations L1 and L2). Larvae from the southern
boundary did settle near the southern populations in
the early fall and late winter. Movement of southern
larvae to populations in the northern half of the domain
occurred only in August and January.
Interannual variability (simulation L4): A comparison
between larval transport simulations of the early fall of
2003 and 2006 revealed that, aside from a difference in
the mean settlement rate, connectivity patterns were
quite similar. Seven of the populations had more than
50% of their larvae settle to the south in both years.
More than 50% of the larvae from three other populations settled to the north in both years. The remaining
three populations switched between predominantly
southward or northward movement of larvae.
Comparing population genetic model simulations and
observed genetic spatial patterns
Summer connectivity base simulation (G1): During simulations for the summer season, when observed larval settlement is generally highest (Gaines et al. 1985;
Broitman et al. 2008), gene flow is predominately north
to south. There is so little gene flow from the south in
this simulation that gene frequencies quickly shift to be
similar to the frequency of the northern gene pool
(Fig. 3A). Only the population at the southernmost site
(AM) retains a higher frequency of southern alleles, in
this case entirely due to the input imposed from the
southern gene pool.
Annual connectivity base simulation (G2): Using the
cumulative connectivity patterns across an entire year
(L2 matrix) causes only minor changes to the predicted
genetic spatial patterns compared to G1. Gene frequencies remain nearly identical along the study area
(Fig. 3B). However, the south to north flowing currents
in the winter months increase the frequency of the
southern alleles in the analysis. This is an unrealistic
model in that it assumes that settlement is equivalent in
summer and winter, but even with this assumption, the
pattern of southern alleles does not match the empirical
data. The main result of this and other simple models is
that the expected gene frequencies are more biased
towards the northern alleles than seen in the genetic
data. These results suggest that there is an additional
factor increasing the relative amount of southern alleles
in these populations.
2010 Blackwell Publishing Ltd
S E A S C A P E G E N E T I C S A N D C L I N E S 3701
A
Populations
Fig. 3 Comparison of the seascape genetic model (dashed
lines) to observed data (solid lines) for mtDNA and nuclear
genes (red and blue, respectively). Results based on the connectivity matrix (A) from the main barnacle settlement season
30 January–10 July, and (B) averaged over the whole year.
Summer connectivity plus southern larval input (G3):
Input of larvae from beyond the southern boundary of
the model can substantially improve the fit between the
genetic model’s predictions and empirical observations.
We conducted simulations with southern input equal to
5–50% of the total settlement recorded in the annual
settlement model. If southern input equals 10–20% of
the input seen during summer, then the resulting
genetic clines are similar to the observed clines except
at PSB where the model predicts gene frequencies that
are much more southern than those observed (Fig. 4A
shows southern input at 15%). Artificially increasing
the direct gene flow from PP to PSB has little effect.
However, adding input into PSB directly from beyond
the northern edge of the model can help resolve this
difference (not shown).
Summer connectivity plus larval retention (G4): Because
the ROMS model only considers ocean movement
beyond approximately 2 km from shore, it cannot be
used to simulate larvae that stay closer than this to the
shoreline. If such larvae also tend to disperse low distances, our model may underestimate the proportion of
larvae that settle close to home. To address this possibility, we added a multiplier (RM) to all pjj values in
order to increase the proportion of larvae that settle at
the same locality at which they are released. We also
adjusted pj,total values to take the higher pjj values into
2010 Blackwell Publishing Ltd
Genetic clines based on summer connectivity plus
15% southern input and 150 times higher local retention
1.2
1
0.8
0.6
COI - 150x retention + 15% S input
EF1 - 150x retention + 15% S input
COI observed
EF1 observed
0.4
0.2
0
AM
AM
SP
CP
SWC
APP
PG
MLJ
CB
TP
SHB
WC
PP
PSB
0
C
SP
0.2
0
CP
COI - all year
EF1 - all year
COI observed
EF1 observed
SWC
0.4
COI - 150x retention
EF1 - 150x retention
COI observed
EF1 observed
0.4
0.2
APP
0.6
0.6
PG
0.8
1
0.8
MLJ
1
1.2
CB
Genetic clines based on annual connectivity
1.2
TP
B
Genetic clines based on summer connectivity
plus 150 times higher local retention
B
SHB
COI - summer
EF1 - summer
COI observed
EF1 observed
0.2
COI - 15% S input
EF1 - 15% S input
COI observed
EF1 observed
PP
0.4
1.2
1
0.8
0.6
0.4
0.2
0
WC
0.6
0
Frequency of haplotype C
Frequency of haplotype C
0.8
Frequency of haplotype C
Frequency of haplotype C
1
Genetic clines based on summer connectivity
plus 15% input from the southern boundary
PSB
Genetic clines based on summer connectivity
1.2
Frequency of haplotype C
A
Populations
Fig. 4 (A) A comparison of observed and predicted genetic
patterns if 15% of the settlers come from beyond the southern
boundary of the ocean model, (B) a comparison of observed
and predicted genetic data based on 150-fold higher retention
than allowed in the simple ocean model, and (C) a combination of higher retention and southern input.
account. Increasing local retention by an order of magnitude (e.g. RM = 10) had little effect on the results.
Increasing it by up to 150-fold brought the observed
and expected genetic data into close agreement
(Fig. 4B).
Summer connectivity plus southern input and larval retention (G5): The above analyses suggest that additional
input of larvae from the south and higher rates of local
retention can substantially improve the fit of our current model (Fig. 4A,B). To explore what combinations
of these factors may best improve fit, we ran our genetics model with various combinations of southern input
from 0% to 20% of the total, and various levels of our
RM from 1 to 300 (e.g. Fig. 4C). We evaluated each
model by comparing the predicted genetic cline with
the observed cline using least-squares criteria. We
summed the squared difference between observed and
predicted gene frequencies at each location, and compared the sums across different models (Fig. 5). Our
original model, with no extra retention or southern
input, produced the largest difference between mod-
3702 H . M . G A L I N D O E T A L .
than the best hypothetical retention regime (Mean
squared difference = 0.08 rather than 0.04), mainly
because the selection regime is assumed to be identical
across Monterey Bay. A more complex selection regime
may perform better. However, it is clear than hypothetical selection and hypothetical retention are both powerful solutions to the difference between the basic model
and the observed data.
Discussion
Fig. 5 The fit of the observed and predicted genetic data (estimated as the sum of least squares) for various combinations of
the RM and the fraction of southern input. Higher values represent a poorer fit of predicted and observed genetic patterns.
Even moderate amounts of southern input and extra retention
dramatically improve the data fit.
elled and observed data. Models with 0 < RM < 50 and
southern input over 10% have a much better fit (red
panels, Fig. 5). Models with RM > 50 and southern
input between 0% and 20% have a slightly better fit to
the data (light blue panels in Fig. 5), though the
improvement drops the sum of squares only marginally
from < 0.2 to < 0.1 (see legend in Fig. 5).
Summer connectivity plus selection in favour of southern
alleles (G6): An additional factor that could improve the
fit between the observed and modelled data is natural
selection in favour of southern alleles (s > 0). When
selection at each locus is increased from s = 0 to 0.02
(for EF1) or s = 0.05 (for COI) the fit between the
observed and modelled data gradually improves over
the results from simulations G1 and G2 (Fig. 6). The
best selection regime explains the data more poorly
1
0.8
0.6
COI - s = 0.05
EF1 - s = 0.02
COI observed
EF1 observed
0.4
0.2
AM
SP
CP
SWC
APP
PG
CB
MLJ
TP
SHB
WC
PP
0
PSB
Frequency of haplotype C
Genetic clines based on summer connectivity plus selection
1.2
Populations
Fig. 6 A comparison of observed and predicted genetic patterns if southern C alleles have a relative fitness advantage in
all populations for both COI (s = 5%) and EF1a (s = 2%).
There is an increasing need to evaluate population
genetic data in the light of realistic, local meta-population models (Manel et al. 2003). By integrating biological-physical oceanographic and genetic models, we are
able to gain new insights into larval connectivity patterns relevant to real world scenarios. In this study on
acorn barnacle dispersal in central California, the larval
transport model shows dispersal from north to south
across Monterey Bay, generating enough gene flow to
completely prevent the persistence of the observed
genetic cline. Adding parameters to the population
genetic model dramatically improves the fit to observed
data. If a small fraction of the settling larvae come from
far to the south, or if local retention of larvae is higher
than suggested by the model, then the models predict
the observed cline with greater accuracy (Figs 4 and 5).
Natural selection in favour of southern genotypes also
improves the accuracy of the model predictions (Fig. 6).
Insights gained from simulated and empirical data
mismatches
Using coupled biological–physical oceanographic models to advect larvae as particles with growth and behaviour in three dimensional circulation fields is one
method of predicting the dispersal pathways of larvae.
Through this type of modelling, environmental parameters can be prescribed, the location of each simulated
larva is known over time, and predictions can be made
through repeated model simulations. However, this
modelling has inherent uncertainties: first the biological
models may imperfectly represent important biological
attributes of the system, and second, the physical models may under-resolve features of water flow that are
important to population dynamics. The ability to use
demographic predictions of ocean models to simulate
gene flow allows us to test the validity of coupled biological-physical models using genetic data and to use
the resulting comparisons to refine model predictions
(Galindo et al. 2006). Our results along the central coast
of California suggest that the available connectivity
model is incomplete, and might be improved by further
consideration of at least two factors.
2010 Blackwell Publishing Ltd
S E A S C A P E G E N E T I C S A N D C L I N E S 3703
Local retention
The model simulations presented in this paper include
biological assumptions and geographic limitations that
may underestimate the tendency of larvae to remain
close to their parents. Fine scale foraging and aggregation behaviours (as reviewed by Woodson & McManus
2007) have not yet been parameterized in this, or other
oceanographic models. These fine scale behaviours
would help to maintain the larvae in the vicinity of persistent fronts where food concentrations are often elevated, thus increasing fitness of the larvae and reducing
dispersal distances by hundreds of kilometres. Woodson
et al. (unpublished data, C.B. Woodson, Stanford University, Palo Alto, CA, USA) used 7 years of high-resolution satellite sea surface temperature to identify
regions of high coastal front activity and compared these
to corresponding time series of marine invertebrate
recruitment across the California Current Large Marine
Ecosystem (CCLME). These investigators found that
front probability explained a high percentage of the variance in recruitment for each species. In northern Monterey Bay, a persistent front was identified from satellite
data where the coastal upwelling jet separates from the
coastline. An inclusion of fine scale foraging and aggregation behaviours resulting in increased numbers of larvae at persistent fronts would most likely increase
retention of larvae within the Monterey Bay region.
In addition, our ability to assess the effects of nearshore circulation on local retention of larvae is limited
by the ocean model configuration. The physical circulation fields obtained from ROMS do not include any current patterns inshore of the 50 m isobath ( 2 km from
shore). This means that any larvae that might stay nearshore never enter the model, and are unaccounted for.
Shanks & Shearman (2009) recently concluded from
larval sampling that about half the B. glandula larvae
along the Oregon coast were within 2 km of shore. Such
larvae are more likely to disperse only short distances
(Largier 2003), and so ocean models might be biased
towards higher dispersal than actually exists. Nearshore
circulation patterns will need to be understood and
modelled in greater detail before we can accurately predict the delivery of larvae directly into the intertidal
zone.
Long-distance dispersal
Our simulations may miss input of southern alleles that
travel from beyond the southern boundary of the
model. This input could be due to long distance gene
flow from the south that is beyond the model grid. In a
simulation in which larvae were released along the
southern boundary of the model, few larvae success 2010 Blackwell Publishing Ltd
fully moved north, and these only occurred in the fallwinter. No larvae moved into the model from the
southern boundary during the spring-summer period.
Of the larvae that entered the model, most arrived near
the southern demes, with a dramatic drop off of arrivals in the middle and northern reaches (Fig. S2, Supporting information). One exception was an increase in
movement of larvae from the southern boundary to the
vicinity of PSB, which occurred in one month of the
simulation. There is also a slight increase in southern
delivery to the Monterey Peninsula.
A targeted field study, combining observations of
physical oceanographic structure and processes, as well
as the plankton and barnacle recruitment in north Monterey Bay, indicates that during upwelling relaxation
events persistent fronts can move in a poleward direction resulting in transport of larvae from south to north
(Woodson et al. 2009). Other researchers have suggested that coastally trapped waves, interacting with
topographic features can produce strong secondary
flows in a poleward direction (Auad & Hendershott
1997; Beckenbach & Washburn 2004). It has been suggested that these coastally trapped waves, which travel
at speeds of 75 km day)1 (86 cm s)1) have the potential
to transport barnacle larvae alongshore and poleward
(Pineda & Lopez 2002).
Two other predictions can be made about the genetics
of barnacles in this system if there is long distance gene
flow from both the north and south. First, it should be
possible to detect the inflow of larvae with multi-locus
genotypes characteristic of distant adult populations
(e.g. Brazeau et al. 2005). Second, mixing of larvae from
different source should generate a signal of multi-locus
linkage disequilibrium. The second prediction is met in
these barnacle populations, especially in Monterey Bay
(Sotka & Palumbi 2006).
Identifying realistic combinations of retention and
southern input
By itself, additional retention must be very strong (e.g.
a multiplier > 50) to bring observed and predicted frequencies substantially into alignment (Fig. 5). Likewise,
by itself a large amount of southern input is required
(Fig. 4A). However, when both features are included
together, we see that modest additional retention and
low levels of southern input greatly improve the model
(Fig. 5). In this case, southern input delivers southern
alleles and extra retention reduces the rate at which
they are advected away. For example, a RM of 50 with
a southern input of 5% provides good model fit. However, at this multiplier, settlement into localities is up to
99% from local sources (range 50–99%, mean 75%), a
value higher than recently measured for short-dispersal
3704 H . M . G A L I N D O E T A L .
coral reef fish (Almany et al. 2007). At a RM of 10, and
southern input of 10–15% also provides a good fit to
the data (Fig. 5). When the RM 10, average retention is
about 57% of larvae, which is similar to the data in Almany et al. (2007). Further field observations are
needed to understand whether such rates of retention
are seen in central California marine systems. In particular, there is a need for biological observations, both in
the water column and on the shoreline, combined with
observations of nearshore currents.
Natural selection
The distinction between our modelling results and
genetic data suggests that southern larvae might make
up 10–20% of settlers to balance the steady gene flow
from the north. If this gene flow persisted for long periods of time, then the genetic cline would gradually
decay. We do not see this in our genetic model simulations because we artificially hold northern and southern
gene frequencies constant. Sotka & Palumbi (2006) suggested that natural selection against northern alleles in
the south and southern alleles in the north must be acting, likely through post-settlement mortality, to prevent
the decay of this genetic cline. Such selection, acting
generation after generation might be low enough to be
ecologically difficult to detect (e.g. s < 0.05), yet still be
powerful enough to generate a cline with the width
seen in our study (Sotka & Palumbi 2006). Hare et al.
(2005) point out that unidirectional gene flow can alter
the equilibrium equations often used to predict the balance between gene flow and selection.
The results of our simple selection model indicate
that selection in favor of southern genotypes along the
length of the cline would in fact help bring the model
and observations into alignment. Such selection is in
accord with predictions of cline theory that the broad
differences in gene frequency we see along the west
coast for Balanus glandula are probably due to a balance
of selection and gene flow (Sotka & Palumbi 2006).
The selection levels required are not huge: selection
coefficients favouring southern alleles ranging from 0.02
to 0.05 across loci are sufficient to substantially improve
the model. However, current data show little hint of this
level of selection. Among 869 barnacles sequenced at
Hopkins Marine Station for COI, there was no increase
in southern haplotype frequency with age. Cyprids had
a COI-C frequency of 91.4% (n = 347) whereas juveniles
had a lower frequency of 88.8% (n = 563) and adults
showed a frequency of 89% (n = 58). The same is true
for EF1a data; there is a drop of southern allele frequencies from 70.7% to 69.5% to 68.1% (n = 694, 1126, 118
respectively, unpublished data). These data do not preclude the action of mild selection because our models
suggest that an increase in gene frequencies by only
0.5% per generation can rectify our model and data, and
such tiny differences are difficult to refute. In addition,
selection may occur unevenly at different times and
places. Another possibility not visible before the present
analysis is that a combination of local retention, southern input and selection may be enough to explain the
persistence of the cline and the patterns of gene frequency changes in Monterey Bay. A model that includes
all three parameters suggests that selection as low as
s = 0.01 for COI and a RF factor of 10 can be a good fit
to the data. Further work separating and independently
testing these factors is needed.
Other potential influential factors
Although our analyses show a good fit between the
augmented models and gene frequency patterns, this fit
does not pinpoint the cause of the mismatch between
modelled and observational data. Other possible causes
of the mismatch include possible inaccuracies in the
temperature and chlorophyll levels that control the
planktonic larval duration, the fact that the numerical
model was only run for 1 year, and assumptions in the
population genetics models including the relative unimportance of genetic drift in populations of this size.
Though we cannot rule out these other factors, our current analysis suggests that they play a smaller role than
selection, retention or southern input.
Interannual variability
Interannual differences in current flow are not likely to
erase the basic oceanographic patterns shown in our
connectivity matrices. A paired t-test showed no significant difference (p > 0.05) between the connectivity
matrix calculated for August 2003 and for August 2006.
There is a trend, however, that localities in southern
Monterey Bay tend to send larvae a little further north
in 2003 than in 2006. This may slow the rate of northto-south gene flow, but by itself will not tend to deliver
more southern alleles into the model. Other years, especially those during El Niño events, which results in
more frequent relaxation events in central California
(Chavez et al. 2002) may provide more south-to-north
gene flow than seen in 2006, but such rare years may
not play a major role in genetic patterns. Our general
conclusion is that the delivery of more southern alleles
into central California is likely to be a regular feature of
dispersal, either through a phenomenon like poleward
moving coastally trapped waves, or frequent years in
which northern dispersal dominates. Nevertheless,
ocean dispersal patterns are expected to be somewhat
stochastic from year to year (Siegel et al. 2008) and
2010 Blackwell Publishing Ltd
S E A S C A P E G E N E T I C S A N D C L I N E S 3705
future work on the role of annual variation in accurate
depictions of ocean gene flow is greatly needed.
Oceanography as a predictor of dispersal
Using oceanographic dispersal models to functionally
represent scales relevant to marine conservation efforts
is a challenging problem; with each new study, our scientific community gets a step closer to the answer. It is
important to remember that this is an iterative process,
which requires ongoing discussions between geneticists,
modellers and observationalists. Inadequate data sets
are a major limitation for these important studies. There
is a clear need for interdisciplinary programs, with
multi-scale observations of both physical features of the
environment and genetic patterns, to provide the basis
for coupled physical-biological modelling studies. Specifically, empirical observations of nearshore physical
oceanographic processes and in situ measurements of
larval behaviour, development, and mortality rates are
needed to better parameterize coupled biological–physical models. Increasing the domain of high-resolution
oceanographic models and minimizing edge effects of
model domains will also be important. Finally, empirical field studies should be designed in concert with the
development of biological-physical models to produce
comparable observed and predicted datasets. Together,
these efforts will provide unprecedented insight into
the larval connectivity among marine populations at a
scale relevant to policy and management decisions.
Acknowledgements
We thank E. Jacobs-Palmer, E. Sotka, and V. Alla for assistance
with sample and data collection. We also thank three anonymous reviewers for their comments, which helped us to significantly improve the manuscript. This is contribution 359 from
PISCO, the Partnership for Interdisciplinary Studies of Coastal
Oceans, funded primarily by the Gordon and Betty Moore
Foundation and the David and Lucille Packard Foundation.
Additional funding was provided by the Andrew Mellon
Foundation, Stanford University, and a National Science Foundation Graduate Research Fellowship awarded to H.M. Galindo. The research for Y. Chao was carried out, in part, at the Jet
Propulsion Laboratory, California Institute of Technology,
under contract with the National Aeronautics and Space
Administration (NASA).
References
Almany GR, Berumen ML, Thorrold SR, Planes S, Jones GP
(2007) Local replenishment of coral reef fish populations in a
marine reserve. Science, 316, 742–744.
Anil AC, Kurian J (1996) Influence of food concentration,
temperature and salinity on the larval development of
Balanus Amphitrite. Marine Biology, 127, 115–124.
2010 Blackwell Publishing Ltd
Auad G, Hendershott MC (1997) The low-frequency transport
in the Santa Barbara Channel: Description and forcing.
Continental Shelf Research, 17, 779–802.
Baums IB, Paris CB, Cherubin LM (2006) A bio-oceanographic
filter to larval dispersal in a reef-building coral. Limnology
and Oceanography, 51, 1969–1981.
Beckenbach E, Washburn L (2004) Low-frequency waves in the
Santa Barbara Channel observed by high-frequency radar.
Journal of Geophysical Research-Oceans, 109, C02010.
Bidigare RR, Chai F, Landry MR et al. (2009) Subtropical
ocean ecosystem structure changes forced by North Pacific
climate variations. Journal of Plankton Research, 31, 1131–
1139.
Bousfield EL (1955) Ecological control of the occurrence of
barnacles in the Miramichi estuary. National Museum of
Canada Biological Series Bulletin, 137, 1–69.
Brazeau DA, Sammarco PW, Gleason DF (2005) A multi-locus
genetic assignment technique to assess sources of Agaricia
agaricites larvae on coral reefs. Journal of Marine Biology, 147,
1141–1148.
Broitman BR, Blanchette CA, Menge BA et al. (2008) Spatial
and temporal patterns of invertebrate recruitment along the
west coast of the United States. Ecological Monographs, 78,
403–421.
Brown SK, Roughgarden J (1985) Growth, morphology, and
laboratory culture of larvae of Balanus glandula (Cirripedia:
Thoracica). Journal of Crustacean Biology, 5, 574–590.
Chai F, Barber RT, Lindley ST (1996) Origin and maintenance
of high nutrient condition in the equatorial Pacific. Deep-Sea
Research II, 42, 1031–1064.
Chai F, Dugdale RC, Peng TH, Wilkerson FP, Barber RT
(2002) One dimensional ecosystem model of the equatorial
Pacific upwelling system. Part I: Model development and
silicon and nitrogen cycle. Deep-Sea Research II, 49, 2713–
2747.
Chai F, Liu G, Xue H et al. (2009) Seasonal and interannual
variability of carbon cycle in South China Sea: a threedimensional physical-biogeochemical modeling study.
Journal of Oceanography, 65, 703–720.
Chao Y, Li Z, Farrara JD, Moline MA, Schofield OME,
Majumdar SJ (2008) Synergistic applications of autonomous
underwater vehicles and the regional ocean modeling
system in coastal ocean forecasting. Limnology and
Oceanography, 53, 2251–2263.
Chao Y, Li Z, Farrara J et al. (2009) Development,
implementation and evaluation of a data-assimilative ocean
forecasting system off the central California coast. Deep-Sea
Research II, 56, 100–126.
Chavez FP, Pennington JT, Castro CG et al. (2002) Biological
and chemical consequences of the 1997–1998 El Nino in
central California waters. Progress in Oceanography, 54, 205–
232.
Connolly SR, Menge BA, Roughgarden J (2001) A latitudinal
gradient in recruitment of intertidal invertebrates in the
northeast Pacific Ocean. Ecology, 82, 1799–1813.
Cowen RK, Paris CB, Srinivasan A (2006) Scaling of
connectivity in marine populations. Science, 311, 522–527.
Doyle JD, Jiang Q, Chao Y, Farrara J (2009) High-resolution
real-time modeling of the marine atmospheric boundary
layer in support of the AOSNII field campaign. Deep-Sea
Research II, 56, 87–99.
3706 H . M . G A L I N D O E T A L .
Epperson BK, McRae BH, Scribner K et al. (2010) Utility of
computer simulations in landscape genetics. Molecular
Ecology, 19, 3549–3564.
Franks PJS (1992) Sink or swim: accumulation of biomass at
fronts. Marine Ecology Progress Series, 82, 1–12.
Fujii M., Chai F, Shi L, Inoue H, Ishii M (2009) Seasonal and
interannual variability of carbon cycling in western and
central tropical–subtropical Pacific: a physical-biogeochemical
modeling study. Journal of Oceanography, 65, 689–701.
Gaines S, Brown S, Roughgarden J (1985) Spatial variation in
larval concentrations as a cause of spatial variation in
settlement for the barnacle, Balanus glandula. Oecologia, 67,
267–272.
Gaines S, Gaylord B, Gerber L, Hastings A, Kinlan B (2007)
Connecting places: the ecological consequences of dispersal
in the sea. Oceanography, 20, 90–99.
Galindo H, Olson D, Palumbi S (2006) Seascape genetics: A
coupled oceanographic-genetic model predicts population
structure of Caribbean corals. Current Biology, 16, 1622–1626.
Gilg MR, Hilbish TJ (2003) The geography of marine larval
dispersal: Coupling genetics with fine-scale physical
oceanography. Ecology, 84, 2989–2998.
Grosberg RK (1982) Intertidal zonation of barnacles: the
influence of planktonic zonation of larvae on vertical
distribution of adults. Ecology, 63, 894–899.
Hardy AC, Bainbridge R (1954) Experimental observations on
the vertical migrations of plankton animals. Journal of the
Marine Biological Association of the United Kingdom, 33, 409–
448.
Hare MP, Guenther C, Fagan WF (2005) Nonrandom larval
dispersal can steepen marine clines. Evolution, 59, 2509–2517.
Hartl DL, Clark AG (1997) Principles of population genetics, 3rd
ed. Sinauer Associates Inc., Sunderland, Massachusetts.
Hentschel BT, Emlet RB (2000) Metamorphosis of barnacle
nauplii: effects of food variability and a comparison with
amphibian models. Ecology, 81, 3495–3508.
Kinlan B, Gaines S (2003) Propagule dispersal in marine and
terrestrial environments: A community perspective. Ecology,
84, 2007–2020.
Kool JT, Paris-Limouzy CB, Andréfouët S, Cowen RK (2009)
Complex migration and the development of genetic structure
in subdivided populations: an example from Caribbean coral
reef ecosystems. Ecography, 32, 1–10.
Largier JL (2003) Considerations in estimating larval dispersal
distances from oceanographic data. Ecological Applications, 13,
S71–S89.
Li Z, Chao Y, McWilliams JC, Ide K (2008) A threedimensional variational data assimilation scheme for the
Regional Ocean Modeling System: implementation and basic
experiments. Journal of Geophysical Research, 113, C05002.
Li Z, Chao Y, McWilliams JC, Ide K (2009) A threedimensional variational data assimilation scheme for the
regional Ocean modeling system. Journal of Atmospheric and
Oceanic Technology, 25, 2074–2090.
Liu G, Chai F (2009) Seasonal and interannual variability of
primary and export production in the South China Sea: a
three-dimensional physical-biogeochemical model study.
ICES Journal of Marine Science, 66, 420–431.
Manel S, Schwartz M, Luikart G, Taberlet P (2003) Landscape
genetics: combining landscape ecology and population
genetics. Trends in Ecology and Evolution, 18, 189–197.
McRae BH (2006) Isolation by resistance. Evolution, 60, 1551–
1561.
Palumbi SR (2003) Population genetics, demographic
connectivity, and the design of marine reserves. Ecological
Applications, 13 (1 Supplement), S146–S158.
Pfeiffer-Herbert AS, McManus MA, Raimondi PT, Chao Y,
Chai F (2007) Dispersal of barnacle larvae along the central
California coast: a modeling study. Limnology and
Oceanography, 52, 1559–1569.
Pfeiffer-Hoyt AS, McManus MA (2005) Modeling the effects of
environmental variability on Balanus glandula larval
development. Journal of Plankton Research, 27, 1211–1228.
Pineda J, Lopez M (2002) Temperature, stratification and
barnacle larval settlement in two Californian sites.
Continental Shelf Research, 22, 1183–1198.
Pinsky ML, Montes Jr HR, Palumbi SR (2010) Using isolation
by distance and effective density to estimate dispersal scales
in anemonefish. Evolution, doi:10.1111/j.1558-5646.2010.
01003.x.
Rooker JR, Secor DH, DeMetrio G, Kaufman AJ, Rios AB,
Ticina V (2008) Evidence of trans-Atlantic movement and
natal homing of bluefin tuna from stable isotopes in otoliths.
Marine Ecology Progress Series, 368, 231–239.
Rousset F (1997) Genetic differentiation and estimation of gene
flow from F-statistics under isolation by distance. Genetics,
145, 1219–1228.
Schaefer KM, Fuller DW, Block BA (2007) Movements,
behavior, and habitat utilization of yellowfin tuna (Thunnus
albacares) in the northeastern Pacific Ocean, ascertained
through archival tag data. Marine Biology, 152, 503–525.
Selkoe KA, Gaines SD, Caselle JE, Warner RR (2006) Current
shifts and kin aggregation explain genetic patchiness in fish
recruits. Ecology, 87, 3082–3094.
Selkoe KA, Henzler CM, Gaines SD (2008) Seascape genetics
and the spatial ecology of marine populations. Fish and
Fisheries, 9, 363–377.
Selkoe KA, Watson JR, White C et al. (2010) Taking the chaos
out of genetic patchiness: seascape genetics reveals
ecological and oceanographic drivers of genetic patterns in
three temperate reef species. Molecular Ecology, 19, 3708–
3726.
Shanks AL, Shearman RK (2009) Paradigm lost? Cross-shelf
distributions of intertidal invertebrate larvae are unaffected
by upwelling or downwelling Marine Ecology Progress Series,
385, 189–204.
Shanks AL, Grantham BA, Carr MH (2003) Propagule
dispersal distance and the size and spacing of marine
reserves. Ecological Applications, 13(1 Supplement), S159–S169.
Shchepetkin AF, McWilliams JC (2005) The Regional Oceanic
Modeling System: A split-explicit, free-surface, topographyfollowing-coordinate ocean model. Ocean Modelling, 9, 347–
404.
Siegel DA, Mitarai S, Costello CJ et al. (2008) The stochastic
nature of larval connectivity among nearshore marine
populations. Proceedings of the National Academy of Sciences,
105, 8974–8979.
Sotka EE, Palumbi SR (2006) The use of genetic clines to
estimate dispersal distances of marine larvae. Ecology, 87,
1094–1103.
Sotka EE, Wares JP, Barth JA, Grosberg RK, Palumbi SR (2004)
Strong genetic clines and geographical variation in gene flow
2010 Blackwell Publishing Ltd
S E A S C A P E G E N E T I C S A N D C L I N E S 3707
in the rocky intertidal barnacle Balanus glandula. Molecular
Ecology, 13, 2143–2156.
Strathmann RR, Branscomb ES, Vedder K (1981) Fatal errors in
set as a cost of dispersal and the influence of intertidal flora
on set of barnacles. Oecologia, 48, 13–18.
Thorrold SR, Latkoczy C, Swart PK, Jones CM (2001) Natal
homing in a marine fish metapopulation. Science, 291, 297–
299.
Thorrold SR, Zacherl DC, Levin LA (2007) Population
connectivity and larval dispersal using geochemical
signatures in calcified structures. Oceanography, 20, 80–89.
Treml EA, Halpin PN, Urban DL, Pratson LF (2008) Modeling
population connectivity by ocean currents, a graph-theoretic
approach for marine conservation. Landscape Ecology, 23, 19–
36.
Veliz D, Duchesne P, Bourget E, Bernatchez L (2006) Stable
genetic polymorphism in heterogeneous environments:
balance between asymmetrical dispersal and selection in
the acorn barnacle. Journal of Evolutionary Biology, 19, 589–
599.
Wang X, Chao Y, Dong C et al. (2009) Modeling tides
in Monterey Bay, California. Deep-Sea Research II, 56, 219–
231.
Weng KC, O’Sullivan JB, Lowe CG, Winkler CE, Dewar H,
Block BA (2007) Movements, behavior and habitat
preferences of juvenile white sharks Carcharodon carcharias
in the eastern Pacific. Marine Ecology Progress Series, 338,
211–224.
Werner FE, Cowen RK, Paris CB (2007) Coupled biological and
physical models. Oceanography, 20, 54–69.
Whitlock M, McCauley D (1999) Indirect measures of gene
flow and migration: F-ST not equal 1 ⁄ (4Nm+1). Heredity, 82,
117–125.
Woodson CB, McManus MA (2007) Foraging behavior can
influence dispersal of marine organisms. Limnology and
Oceanography, 52, 2701–2709.
Woodson CB, Washburn L, Barth JA et al. (2009) The northern
Monterey Bay upwelling shadow front: observations of a
coastally- and surface-trapped buoyant plume. Journal of
Geophysical Research, doi:10.1029/2009JC005623.
Wright S (1931) Evolution in Mendelian populations. Genetics,
16, 97–159.
2010 Blackwell Publishing Ltd
H.G. is a post-doc at the University of Washington using molecular ecology and coupled oceanographic-genetic models to
study spatial connectivity among marine populations in a management context. A.P.H. is a PhD candidate at the University
of Rhode Island’s Graduate School of Oceanography whose
research focuses on biological-physical interactions in coastal
marine ecosystems. M.A.M. is an Associate Professor in the
Department of Oceanography at the University of Hawaii at
Manoa. She is recognized for her work assessing the effects of
physical oceanography on the dynamics of marine ecosystems
in the coastal environment. Y.C. is a physical oceanographer at
the Jet Propulsion Laboratory at California Institute of Technology who uses numerical ocean models to study the 3D ocean
circulation and variability including its response to weather
and climate and impact on biogeochemistry and marine ecosystem. F.C. is an oceanographer at the University of Maine
who develops and uses physical-biological models to investigate carbon cycle and marine ecosystem responses to climate
change. S.P. works on the interface between population and
evolutionary biology in marine systems using genetic tools.
Supporting information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Population connectivity matrix for 15 August 2006–29
January 2007. Columns are site of larval release and rows are
settlement site.
Fig. S2 Distribution of settlers from the southern boundary of
the ocean model based on the annual connectivity matrix.
Table S1 Haplotype clade frequences for EF1a determined by
DNA sequence at basepairs 105–108 relative to the 156bp fragment in Sotka et al. (2004). (Clade A=TCCA, Clade B=TCAT,
Clade C=CCCA.)
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